{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Trading Rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sysdata.sim.csv_futures_sim_data import csvFuturesSimData\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Work up a minimum example of a trend following system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get some data\n",
    "\n",
    "We can get data from various places; however for now we're going to use\n",
    "prepackaged 'legacy' data stored in csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No datapaths provided for .csv, will use defaults  (may break in production, should be fine in sim)\n",
      "No datapaths provided for .csv, will use defaults  (may break in production, should be fine in sim)\n",
      "No datapaths provided for .csv, will use defaults  (may break in production, should be fine in sim)\n",
      "No datapaths provided for .csv, will use defaults  (may break in production, should be fine in sim)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "csvFuturesSimData object with 46 instruments"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = csvFuturesSimData()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get stuff out of data with methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AEX', 'AUD', 'BITCOIN', 'BOBL', 'BTP', 'BUND', 'CAC', 'COPPER', 'CORN', 'CRUDE_W', 'CRUDE_W_mini', 'EDOLLAR', 'EUR', 'EUROSTX', 'GAS_US', 'GAS_US_mini', 'GBP', 'GOLD', 'GOLD_micro', 'JPY', 'KOSPI', 'KOSPI_mini', 'KR10', 'KR3', 'LEANHOG', 'LIVECOW', 'MXP', 'NASDAQ', 'NASDAQ_micro', 'NZD', 'OAT', 'PALLAD', 'PLAT', 'SHATZ', 'SMI', 'SOYBEAN', 'SP500', 'SP500_micro', 'US-REALESTATE', 'US10', 'US2', 'US20', 'US5', 'V2X', 'VIX', 'WHEAT']\n",
      "index\n",
      "2021-03-08 16:00:00    98.865\n",
      "2021-03-08 17:00:00    98.860\n",
      "2021-03-08 18:00:00    98.865\n",
      "2021-03-08 19:00:00    98.865\n",
      "2021-03-08 20:00:00    98.860\n",
      "Name: price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(data.get_instrument_list())\n",
    "print(data.get_raw_price(\"EDOLLAR\").tail(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data can also behave in a dict like manner (though it's not a dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index\n",
       "1997-12-12 23:00:00    1081.25\n",
       "1997-12-15 23:00:00    1089.25\n",
       "1997-12-16 23:00:00    1095.25\n",
       "1997-12-17 23:00:00    1089.25\n",
       "1997-12-18 23:00:00    1079.25\n",
       "                        ...   \n",
       "2021-03-08 17:00:00    3869.50\n",
       "2021-03-08 18:00:00    3845.50\n",
       "2021-03-08 19:00:00    3849.75\n",
       "2021-03-08 20:00:00    3820.00\n",
       "2021-03-08 21:00:00    3822.50\n",
       "Name: price, Length: 16153, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SP500']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AEX',\n",
       " 'AUD',\n",
       " 'BITCOIN',\n",
       " 'BOBL',\n",
       " 'BTP',\n",
       " 'BUND',\n",
       " 'CAC',\n",
       " 'COPPER',\n",
       " 'CORN',\n",
       " 'CRUDE_W',\n",
       " 'CRUDE_W_mini',\n",
       " 'EDOLLAR',\n",
       " 'EUR',\n",
       " 'EUROSTX',\n",
       " 'GAS_US',\n",
       " 'GAS_US_mini',\n",
       " 'GBP',\n",
       " 'GOLD',\n",
       " 'GOLD_micro',\n",
       " 'JPY',\n",
       " 'KOSPI',\n",
       " 'KOSPI_mini',\n",
       " 'KR10',\n",
       " 'KR3',\n",
       " 'LEANHOG',\n",
       " 'LIVECOW',\n",
       " 'MXP',\n",
       " 'NASDAQ',\n",
       " 'NASDAQ_micro',\n",
       " 'NZD',\n",
       " 'OAT',\n",
       " 'PALLAD',\n",
       " 'PLAT',\n",
       " 'SHATZ',\n",
       " 'SMI',\n",
       " 'SOYBEAN',\n",
       " 'SP500',\n",
       " 'SP500_micro',\n",
       " 'US-REALESTATE',\n",
       " 'US10',\n",
       " 'US2',\n",
       " 'US20',\n",
       " 'US5',\n",
       " 'V2X',\n",
       " 'VIX',\n",
       " 'WHEAT']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... however this will only access prices\n",
    "(note these prices have already been backadjusted for rolls)\n",
    "\n",
    "We have extra futures data here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRICE</th>\n",
       "      <th>CARRY</th>\n",
       "      <th>PRICE_CONTRACT</th>\n",
       "      <th>CARRY_CONTRACT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-03-08 15:00:00</th>\n",
       "      <td>98.865</td>\n",
       "      <td>99.015</td>\n",
       "      <td>20231200</td>\n",
       "      <td>20230900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-08 16:00:00</th>\n",
       "      <td>98.865</td>\n",
       "      <td>99.020</td>\n",
       "      <td>20231200</td>\n",
       "      <td>20230900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-08 17:00:00</th>\n",
       "      <td>98.860</td>\n",
       "      <td>99.015</td>\n",
       "      <td>20231200</td>\n",
       "      <td>20230900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-08 18:00:00</th>\n",
       "      <td>98.865</td>\n",
       "      <td>99.020</td>\n",
       "      <td>20231200</td>\n",
       "      <td>20230900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-08 19:00:00</th>\n",
       "      <td>98.865</td>\n",
       "      <td>99.015</td>\n",
       "      <td>20231200</td>\n",
       "      <td>20230900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-08 20:00:00</th>\n",
       "      <td>98.860</td>\n",
       "      <td>99.010</td>\n",
       "      <td>20231200</td>\n",
       "      <td>20230900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      PRICE   CARRY PRICE_CONTRACT CARRY_CONTRACT\n",
       "index                                                            \n",
       "2021-03-08 15:00:00  98.865  99.015       20231200       20230900\n",
       "2021-03-08 16:00:00  98.865  99.020       20231200       20230900\n",
       "2021-03-08 17:00:00  98.860  99.015       20231200       20230900\n",
       "2021-03-08 18:00:00  98.865  99.020       20231200       20230900\n",
       "2021-03-08 19:00:00  98.865  99.015       20231200       20230900\n",
       "2021-03-08 20:00:00  98.860  99.010       20231200       20230900"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.get_instrument_raw_carry_data(\"EDOLLAR\").tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Technical note: csvFuturesSimData inherits from FuturesData which itself inherits from simData\n",
    "The chain is 'data specific' <- 'asset class specific' <- 'generic'\n",
    "\n",
    "Let's create a simple trading rule\n",
    "\n",
    "No capping or scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sysquant.estimators.vol import robust_vol_calc\n",
    "\n",
    "\n",
    "def calc_ewmac_forecast(price, Lfast, Lslow=None):\n",
    "    \"\"\"\n",
    "    Calculate the ewmac trading rule forecast, given a price and EWMA speeds\n",
    "    Lfast, Lslow and vol_lookback\n",
    "\n",
    "    \"\"\"\n",
    "    # price: This is the stitched price series\n",
    "    # We can't use the price of the contract we're trading, or the volatility\n",
    "    # will be jumpy\n",
    "    # And we'll miss out on the rolldown. See\n",
    "    # https://qoppac.blogspot.com/2015/05/systems-building-futures-rolling.html\n",
    "\n",
    "    price = price.resample(\"1B\").last()\n",
    "\n",
    "    if Lslow is None:\n",
    "        Lslow = 4 * Lfast\n",
    "\n",
    "    # We don't need to calculate the decay parameter, just use the span\n",
    "    # directly\n",
    "    fast_ewma = price.ewm(span=Lfast).mean()\n",
    "    slow_ewma = price.ewm(span=Lslow).mean()\n",
    "    raw_ewmac = fast_ewma - slow_ewma\n",
    "    vol = robust_vol_calc(price.diff())\n",
    "    return raw_ewmac / vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Try it out\n",
    "\n",
    "(this isn't properly scaled at this stage of course)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index\n",
       "2021-03-02   -1.961088\n",
       "2021-03-03   -2.065046\n",
       "2021-03-04   -2.260685\n",
       "2021-03-05   -2.515280\n",
       "2021-03-08   -2.784115\n",
       "Freq: B, Name: price, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instrument_code = 'EDOLLAR'\n",
    "price = data.daily_prices(instrument_code)\n",
    "ewmac = calc_ewmac_forecast(price, 32, 128)\n",
    "ewmac.columns = ['forecast']\n",
    "ewmac.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ewmac.plot();\n",
    "plt.title('Forecast')\n",
    "plt.ylabel('Position')\n",
    "plt.xlabel('Time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Did we make money?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from systems.accounts.account_forecast import pandl_for_instrument_forecast\n",
    "account = pandl_for_instrument_forecast(forecast=ewmac, price = price)\n",
    "account.curve().plot();\n",
    "plt.title('Profit and Loss')\n",
    "plt.ylabel('PnL')\n",
    "plt.xlabel('Time');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('min', '-5.81'),\n",
       "  ('max', '5.141'),\n",
       "  ('median', '0'),\n",
       "  ('mean', '0.01502'),\n",
       "  ('std', '0.5036'),\n",
       "  ('skew', '-0.1824'),\n",
       "  ('ann_mean', '3.846'),\n",
       "  ('ann_std', '8.057'),\n",
       "  ('sharpe', '0.4773'),\n",
       "  ('sortino', '0.5659'),\n",
       "  ('avg_drawdown', '-11.37'),\n",
       "  ('time_in_drawdown', '0.9738'),\n",
       "  ('calmar', '0.1078'),\n",
       "  ('avg_return_to_drawdown', '0.3382'),\n",
       "  ('avg_loss', '-0.3198'),\n",
       "  ('avg_gain', '0.33'),\n",
       "  ('gaintolossratio', '1.032'),\n",
       "  ('profitfactor', '1.107'),\n",
       "  ('hitrate', '0.5175'),\n",
       "  ('t_stat', '2.935'),\n",
       "  ('p_value', '0.003342')],\n",
       " ('You can also plot / print:',\n",
       "  ['rolling_ann_std', 'drawdown', 'curve', 'percent'])]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account.percent.stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
